17th Wood NDT International Nondestructive Testing and Evaluation of Wood Symposium, Sopron, Hungary, 14th-16th September 2011

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1 17th Wood NDT International Nondestructive Testing and Evaluation of Wood Symposium, Sopron, Hungary, 14th-16th September 2011 Factors and models for the grade distributions of sawn timber for Scots pine and Norway spruce in Finland and north-western Russia: visual strength and appearance quality Sanna Hautamäki, Researcher, Dr. Sc. student The Finnish Forest Research Institute

2 Introduction There is a further need for efficient sorting and grading of timber which utilizes the whole variation of mechanical properties of wood. Early allocation of raw material ( seeing inside the log ) is a crucial part of the efficient procurement chain. Wood is competing with other materials, such as steel, concrete, plastics and composites in construction, as well as in other uses.

3 Introduction Sorting and grading of sawn timber is used to make timber trade more effective. Grading methods include, for example, INSTA 142 rules for visual strength and NT grading for appearance grading for nordic timber, as well upcoming EN 338 rules (C grades). In addition, sawmills use their own, endproduct oriented and internal grading rules for both logs and sawn timber.

4 Objectives Study the variation and predictability of grade distributions for visual strength and appearance grades of Scots pine and Norway spruce timber from Finland and north-western Russia. Find out how accurately it is possible to predict the grade yields from either log properties or sawn timber properties, and whether any regional differences remain thereafter.

5 Materials Data consisted of 1,162 spruce and 1,069 pine sawn pieces from selected log procurement areas in Finland and north-western Russia.

6 Materials Scots pine Region Diameter class of the log with bark, min top diameter, mm Dimension of the sawn piece, mm 38*100 50*100 50*150 63*200 44*200 Total South-eastern Finland Western Finland Northern Finland Novgorod, Russia Vologda, Russia ,069

7 Materials Norway Spruce Region Diameter class of the log with bark, min top diameter, mm Dimension of the sawn piece, mm 38*100 50*100 50*150 63*200 44*200 South-eastern Finland Western Finland Northern Finland Vologda, Russia Karelia,Russia ,162

8 Materials Several measurements were conducted on logs and sawn timber, such as log type, knot features, ring width, latewood, heartwood, grain angle and technical defects. Sawn pieces were visually graded for T grades T10, T18, T24, T30 (and T40 for pine), which is a Finnish application of the INSTA 142 rules, as well as appearance graded to grades A, B, C and D according to the Nordic timber grading rules (Lipitsäinen 1994, Nordic Timber 1994).

9 Methods Since the response variable is categorical, logistic and multinomial regression were chosen as modeling methods. The method seeks to predict the probability/odds ratio of belonging to a certain grade compared to the reference grade; the probability of success or failure of an event occurring.

10 Methods The logistic regression has a linear form for the logit of this probability. Results are often expressed as odds ratios, that is, the ratio of the odds of being in category 1 to the reference category (e.g Agresti 1996). Multinomial regression is an expansion of the logistic regression, where more than two response categories are possible.

11 Logistic models: T30 tot40 or T24 tot40 comprised category 1, and lower grades category 0. Multinomial models: strength grades from T10 tot40 for pine and from T10 tot30 for spruce were modeled using the best grade (T40 or T30) as the reference. Multinomial appearance grade models were calculated for pine only, because of the dominant proportion of grade B for spruce in the grade yield. For pine, the grade A was the reference. Methods

12 Proportion of pine sawn pieces Descriptive results 100% 90% 80% 70% 60% 50% 40% 30% T10 T18 T24 T30 T40 20% 10% 0% South-eastern Finland Western Finland Northern Finland Novgorod Vologda Region

13 Proportion of spruce sawn pieces Descriptive results 100% 90% 80% 70% 60% 50% 40% 30% 20% T10 T18 T24 T30 10% 0% South-eastern Finland Western Finland Northern Finland Republic of Karelia Vologda Region

14 Proportion of pine sawn pieces Descriptive results 100% 90% 80% 70% 60% 50% 40% 30% D C B A 20% 10% 0% South-eastern Finland Western Finland Northern Finland Novgorod Vologda Region

15 Proportion of spruce sawn pieces Descriptive results 100% 90% 80% 70% 60% 50% 40% 30% D C B A 20% 10% 0% South-eastern Finland Western Finland Northern Finland Republic of Karelia Vologda Region

16 Proportion of pine logs Descriptive results High quality butt log Lower quality butt log Upper log 100% 90% 80% 70% 60% 50% 40% 30% 20% 10% 0% South-eastern Finland Western Finland Northern Finland Novgorod Vologda Region

17 Proportion of spruce logs Descriptive results High qualty butt log Lower quality butt log Normal quality upper log Lower quality upper log 100% 90% 80% 70% 60% 50% 40% 30% 20% 10% 0% South-eastern Finland Western Finland Northern Finland Republic of Karelia Vologda Region

18 Modeling results Multinomial models, Scots pine T40 T30 T24 T18 T10 Overall T grading, log properties T grading, sawn timberproperties A B C D Overall NT grading, log properties NT grading, sawn timber properties Logistic models, Scots pine T30- T40 T24- T40 Others Overall T grading,t30-t40, log properties T grading, T24-T40, log properties T grading, T30-T40,sawn timber properties T grading, T24-T40, sawn timber properties

19 Modeling results Multinomial models, Norway spruce T30 T24 T18 T10 Overall T grading, log properties T grading, sawn timber properties Logistic models, Norway spruce T24-T30 Others Overall T grading, T24-T30, log properties T grading, T24-T30, sawn timber properties

20 Factors for grade distributions Differences between geographical regions did appear in this study, especially for pine, however, not being the major grade determinig factor. All regions did not appear significant in all submodels. For spruce, region was mostly insignificant.

21 Factors for grade distributions Region Multinomial models Logistic models Pine, T grades Odds ratios Odds ratios Western Finland South-eastern Finland (Reference: Vologda) Pine, NT grades Western Finland South-eastern Finland (Reference: Vologda) Spruce, T grades South-eastern Finland (Reference: Vologda) All odds ratios presented are significant at 5% level.

22 Factors for grade distributions The external quality and type of the log (butt logs versus upper logs) were the most important grade determining factors when log properties were used as predictors. Butt logs generally influenced towards better grades compared to upper logs.

23 Factors for grade distributions Log type Multinomial models Logistic models Pine, T grades Odds ratios Odds ratios High or low quality butt log (Reference: Upper log) Pine, NT grades High or low quality butt log (Reference: Upper log) Spruce, T grades Butt log or higher quality upper log (Reference: lower quality upper log)

24 Factors for grade distributions Knot related properties were important predictors, especially when sawn timber properties were used as predictors: the diameter and number of sound, or dry knots on sawn piece. The largest dry knot on the log was the most important variable when using log properties as predictors.

25 Factors for grade distributions Knot related properties Multinomial models Logistic models Pine, T grades Odds ratios Odds ratios Largest dry knot on log, mm Largest sound knot on log, mm Number of sound knots on sawn piece Number of dry knots on sawn piece Largest knot on sawn piece, mm Latewood % Pine, NT grades Largest dry knot on log, mm Largest knot on sawn piece, mm

26 Factors for grade distributions Spruce, T grades Multinomial models Odds ratios Logistic models Odds ratios Number of small knots on sawn piece Largest dry knot on log, mm Largest sound knot on log Largest sound knot on sawn piece Number of sound knots on sawn piece Number of resin pockets 0.792

27 Conclusions Variation in measured properties and geographical differences were larger for pine than for spruce, therefore the procurement area is less important when acquiring spruce timber.

28 Conclusions More best and worst grades can be expected from pine, and more intermediate timber from spruce because of the homogeneity of the timber properties of spruce. In this study, timber from northern Finland suffered from dry knots compared to more southern origins.

29 Conclusions The effect of climate and forestry practises came through in the measured properties and grade yield distributions; generally, colder climate (northern Finland and Vologda) provides better grades. Forestry practises, such as thinnings, affect the grade yield clearly between more fertile origins in Finland and Russia: this appeared in the size of dry knots on the logs and sawn pieces.

30 Conclusions Technical defects of sawn timber which were not included in the models, but cause serious downgrading, such as knot sum (KAR) and new shoot formation (top break), affect the performance of the spruce models. Edge knots still remain the main cause for downgrading, but were not unfortunately included in the models. The data of this study was commercially obtained, and therefore objective sampling and regional representability was not fully met, especially in the Russian areas.

31 Further research Studying factors and generating models for modulus of elasticity and bending strength using log and/or sawn timber properties as predictors, Studying factors for and modeling the grade yields of machine graded strength classes C14 to C50 and rejects. In both studies, the datasets from the same logs and parallel pieces of sawn timber from the same geographical regions as in this study are used.

32 Literature Agresti, A An introduction to categorical data analysis. John Wiley & Sons. New York. 290 p. Hautamäki, S., Kilpeläinen, H., Kannisto, K., Wall, T. and Verkasalo, E Factors Affecting the Appearance Quality and Visual Strength Grade Distributionsof Scots Pine and Norway Spruce Sawn Timber in Finland and North-Western Russia. Baltic Forestry Vol. 16, No. 2. Lindgren, C Sahatavaran visuaalinen ja koneellinen lujuuslajittelu. [Visual strength grading and machine strength grading of Finnish sawn timber]. VTT Publications 820, 79 p. + App. 11 p. In Finnish. Lipitsäinen Lujuslajitteluohjeet T40 T30 T24 T18. [Strength Grading Instructions], Lujuuslajittelukurssit. VTT Rakennustekniikka. Puutekniikka. Julkaisu 2/ pp. In Finnish Lukkarinen, A., Verkasalo, E. and Riekkinen, M Comparison between predicted and measured strength and stiffness of Norway spruce lumber from Finland and north-west Russia. In: 14 th International Symposium on Nondestructive Testing of Wood, 2 nd 4 th May 2005, Hannover, Germany, Abstracts, p. 54. Powerpoint, 14 pp. Nordic Timber Grading Rules Föreningen Svenska Sågverksmann. Suomen Saheteollisuusmiesten yhdistys. Treindustriens Tekniske Forening. Stockholm, Helsinki, Oslo, 64 pp. Verkasalo, E., Kilpeläinen, H., Wall, T and Hanhijärvi, A. 2007b. Venäjän tuontikuusen laatu ja arvo vaativissa rakennustuotteissa ja rakennuspuusepäntuotteissa Vertailukohtana suomalainen kuusi [The value and quality of imported russian spruce timber in demanding construction and carpentry end-uses A comparison with Finnish spruce]. 34 p. In Finnish and Russian. [Cited ]. Available: Virtanen, J Analysing sawnwood supply distribution in Finnish sawmilling industry with database approach. Helsinki University of Technology, Department of Forest Products Technology, Laboratory of Wood Technology, Report p + App. 15 p.

33 Acknowledgements This study was a part of the develpment projects Wood quality in Russia and bilateral information service in forestry sector ( ) and Effects of internationalizaton of roundwood markets and business startegies of sawmilling industry in Finland ( ). This study was a joint project of the Finnish Forest Research Institute, Technical Research Institute of Finland (VTT) and Kymenlaakso University of Applied Sciences, and gratitude is expressed to Prof. Alpo Ranta-Maunus, Dr. Antti Hanhijärvi from VTT and Dr. Hannu Boren from Kymenlaakso University of Applied Sciences.

34 Thank you